关键词： Location privacy protection   Maritime network   Mobile edge computing   Task floading  

摘要： Mobile Edge Computing(MEC)can support various high-reliability and low-delay applications in Maritime Networks(MNs).However,security risks in computing task offloading *** this study,the location privacy leakage risk of Maritime Mobile Terminals(MMTs)is quantified during task offloading and relevant Location Privacy Protection(LPP)schemes of MMT are considered under two kinds of task offloading *** single-MMT and single-time offloading scenario,a dynamic cache and spatial cloaking-based LPP(DS-CLP)algorithm is proposed;and under the multi-MMTs and multi-time offloading scenario,a pseudonym and alterable silent period-based LPP(PA-SLP)strategy is *** results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location ***,extending the alterable silent period,increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in ***,the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.

关键词： mobile edge computing   computation offloading   caching   resource allocation  

摘要： Applications with sensitive delay and sizeable data volumes,such as interactive gaming and augmented reality,have become popular in recent *** applications pose a huge challenge for mobile users with limited *** offloading is a mainstream technique to reduce execution delay and save energy for mobile ***,computation offloading requires communication between mobile users and mobile edge computing(MEC) *** a mechanism would difficultly meet users’ demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for *** task data can effectively reduce the data transmissions when users offload their tasks to the MEC *** limited caching space at the MEC server calls for judiciously decide which tasks should be *** by this,we consider the joint optimization of computation offloading and task caching in a cellular *** particular,it allows users to proactively cache or offload their tasks at the MEC *** objective of this paper is to minimize the system cost,which is defined as the weighted sum of task execution delay and energy consumption for all *** at establishing optimal performance bound for the system design,we formulate an optimization problem by jointly optimizing the task caching,computation offloading,and resource *** problem is a challenging mixed-integer non-linear programming problem and is NP-hard in *** solve it efficiently,by using convex optimization,Karmarkar ’s algorithm and the proposed fast search algorithm,we obtain an optimal solution of the formulated problem with manageable computational *** simulation results show that in comparison to some representative benchmark methods,the proposed solution can effectively reduce the system cost.

关键词： mobile edge computing   DDoS attacks   resource-based pricing   game theory   Nash equilibrium  

摘要： Resource-constrainted and located closer to users,edge servers are more vulnerable to Distributed Denial of Service(DDoS)*** order to mitigate the impact of DDoS attacks on benign users,this paper designed a Resource-based Pricing Collaborative approach(RPC)in mobile edge *** introducing the influence of resource prices on requester in economics,a collaboration model based on resource pricing was established,and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in *** article theoretically proved the existence and rationality of the Nash ***,simulation results verified the effectiveness and feasibility of the proposed approach in two experimental *** results shows that RPC can effectively improve the network ability to mitigate DDoS attacks,and alleviate the adverse effects of server attacks under delay constraints.

关键词： Mobile edge computing   Computation floading   Delay sensitivity   Centralized ofloading  

摘要： As a promising computing paradigm,Mobile Edge Computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery capacity and limited bandwidth of the Internet of Things(IoT)*** existing works on mobile edge task ignores the delay sensitivities,which may lead to the degraded utility of computation offloading and dissatisfied *** this paper,we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication ***,we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task ***,we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading(CIRO)algorithm to collect all information in the MEC *** starting with an initial offloading strategy,the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it *** simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

关键词： Mobile edge computing   service reliability   user coverage   application deployment   approximation approach  

摘要： Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget K. We formally define this joint Coverage-Reliability for K-Budgeted Edge Application Deployment (CR-BEAD) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (named BEAD-O) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach named BEAD-G with a constant approximation ratio of 1 - 1/e to solve large-scale CRBEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

关键词： Servers   NOMA   Resource management   Interference   Downlink   5G mobile communication   Base stations   Non-orthogonal multiple access (NOMA)   mobile edge computing   user allocation   game theory  

摘要： Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

关键词： Local delay   mobile edge computing   MEC   stochastic geometry   VANET   urban area  

摘要： This article studies the local delay modeling and analysis in a mobile edge computing (MEC) based vehicular ad hoc network (VANET) for an urban scenario where vehicle nodes request for computing services from an edge node. Based on stochastic geometry, theoretical local delay models are derived for uplink transmission of a service request and downlink transmission of a computing result, respectively. In deriving the theoretical models, the road distribution of an urban area is modeled as a Poisson line process (PLP), the spatial distribution of vehicle nodes on each street are modeled as an independent one-dimensional homogeneous Poisson point process (PPP), and edge nodes are deployed along each street with a certain probability. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access, and independent Nakagami-m fading is considered over all wireless links. Based on the derived models, the impacts of major parameters on the local delay are investigated through numerical results. The derived local delay models can be used to help parameter settings in the deployment of edge nodes in an urban area.

关键词： Mobile edge computing   coded computing   task offloading  

摘要： The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by $(k,r)$ linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable $V$ -structure with respect to the coding parameter $r$ , under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

关键词： Mobile edge computing   Time slot allocation   Wireless power transfer   User cooperation  

摘要： Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the "double near-far" effect. Therefore, in order to further overcome the influence of the "double near-far" effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme;the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Tensors   Training   Convolutional neural networks   Data models   Computational modeling   Servers   Collaborative work   Federated learning   mobile edge computing   traffic sign image recognition   convolutional neural networks  

摘要： Traffic sign recognition and autonomous vehicles computing are a few of the innovative applications which are emerging in the domain of mobile edge computing. Distributed machine learning in the form of Federated Learning (FL) has been applied to mobile edge computing through a range of methodologies and techniques for intelligent feature classification approaches. The challenges that research on such FL methods is facing is twofold: identify an optimal distributed architecture and algorithm components to each side to meet the demand of heavy data processing, and enhance the algorithm components with heuristics that fit to the problem domain and optimize the key parameters of the algorithms. In this prospect, we present a Federated Learning implementation based on a neural network architecture with emphasis to traffic sign image recognition. Our benchmark was tested with two FL strategies seeking an optimal performance model and in reference to a corresponding data set. We present the results of this work while we define the scope of future improvements to our model.